scholarly journals Energy Efficiency Analysis in Modified GoF Spectrum Sensing-Based AF Relay Cooperative Cognitive Sensor Network with Energy Harvesting

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yin Mi ◽  
Guangyue Lu ◽  
Wenbin Gao
Keyword(s):  
Energy Efficiency ◽  
Energy Harvesting ◽  
Sensor Network ◽  
Spectrum Sensing ◽  
Closed Form Expression ◽  
Initial Time ◽  
Maximization Problem ◽  
Power Splitting ◽  
Transmission Power ◽  
Af Relay

In this paper, we propose a joint sensing duration and transmission power allocation scheme to maximize the energy efficiency (EE) of the secondary user (SU) in a cooperative cognitive sensor network (CSN). At the initial time slot of the frame, the secondary transmitter (ST) performs energy harvesting (EH) and spectrum sensing simultaneously using power splitting (PS) protocol. The modified goodness of fit (GoF) spectrum sensing algorithm is employed to detect the licensed spectrum, which is not sensitive to an inaccurate noise power estimate. Based on the imperfect sensing results, the ST will act as an amplify-and-forward (AF) relay and assist in transmission of the primary user (PU) or transmit its own data. The SU’s EE maximization problem is constructed under the constraints of meeting energy causality, sensing reliability, and PU’s quality of service (QoS) requirement. Since the SU’s EE function is a nonconvex problem and difficult to solve, we transform the original problem into a tractable convex one with the aid of Dinkelbach’s method and convex optimization technique by applying a nonlinear fractional programming. The closed-form expression of the ST’s transmission power is also derived through Karush-Kuhn-Tucker (KKT) and gradient method. Simulation results show that our scheme is superior to the existing schemes.

scholarly journals Energy-Efficiency Performance Analysis and Maximization Using Wireless Energy Harvesting in Wireless Sensor Networks

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2917 ◽  
Author(s):  
Zaki Masood ◽  
Sokhee Jung ◽  
Yonghoon Choi
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Wireless Sensor ◽  
Maximization Problem ◽  
Power Splitting ◽  
Power Transfer ◽  
Wireless Power ◽  
Dual Decomposition

Paradigm shift to wireless power transfer provides opportunities for ultra-low-power devices to increase energy storage from electromagnetic (EM) sources. The notable gain occurs when EM sources deliver information as a meaningful signal with power transfer. Thus, energy harvesting (EH) is an active approach to obtain power from surrounding EM sources that transfer energy deliberately. This paper discusses energy efficiency (EE) trade-offs and EE maximization in simultaneous wireless power and information transfer (SWIPT) for wireless sensor networks (WSNs). The power splitting (PS) and time switching (TS) model for SWIPT are investigated in detail, where EE optimization is essential. This work formulates EE maximization problem as non-linear fractional programming and proposes a novel algorithm to solve the maximization problem using Lagrange dual decomposition. Numerical results reveal that the proposed algorithm maximizes EE in both PS and TS modes through noteworthy improvements.

scholarly journals Dynamic Power Splitting Strategy for SWIPT Based Two-Way Multiplicative AF Relay Networks with Nonlinear Energy Harvesting Model

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Tianci Wang ◽  
Guangyue Lu ◽  
Yinghui Ye ◽  
Yuan Ren
Keyword(s):  
Energy Harvesting ◽  
Iterative Method ◽  
Relay Network ◽  
Power Splitting ◽  
Amplify And Forward ◽  
Channel State ◽  
Dynamic Power ◽  
Energy Constrained ◽  
Af Relay ◽  
Nonlinear Energy

This paper investigates an energy-constrained two-way multiplicative amplify-and-forward (AF) relay network, where a practical nonlinear energy harvesting (NLEH) model is equipped at the relay to realize simultaneous wireless information and power transfer (SWIPT). We focus on the design of dynamic power splitting (DPS) strategy, in which the PS ratio is able to adjust itself according to the instantaneous channel state information (CSI). Specifically, we first formulate an optimization problem to maximize the outage throughput, subject to the NLEH. Since this formulated problem is nonconvex and difficult to solve, we further transfer it into an equivalent problem and develop a Dinkelbach iterative method to obtain the corresponding solution. Numerical results are given to verify the quick convergence of the proposed iterative method and show the superior outage throughput of the designed DPS strategy by comparing with two peer strategies designed for the linear energy harvesting (LEH) model.

scholarly journals An Energy-Efficient Spectrum Sensing in Cognitive Radio Network Using Fractional Optimization Model

2021 ◽  
Author(s):  
Praveen Hipparge ◽  
Shivkumar S. Jawaligi
Keyword(s):  
Energy Efficiency ◽  
Wireless Communication ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Spectrum Sensing ◽  
Radio Spectrum ◽  
Energy Utilization ◽  
Transmission Power ◽  
Power Spectral ◽  
Spectrum Holes

Abstract In wireless communication, the main challenge is to use the radio spectrum efficiently. The spectrum used for wireless radio technology is a natural resource that is limited and expensive. The tremendous growth of the market for wireless communication has led to radio spectrum scarcity. The process for conventional spectrum sensing initiates by scanning the frequency spectra for finding the spectrum holes. On the basis of the availability of spectrum holes, Secondary User (SU) can transmit data and need to perform periodic sensing for a seamless connection. In this work, to detect spectrum holes with improved energy utilization, we have proposed the Fractional Optimization Model (FOM) which is the combination of Gray wolf optimization and Cuckoo search algorithm to detect the spectrum holes with improved energy utilization. In this paper, the model is made to obtain energy efficiency while considering different spectrum sensing states. The energy efficiency is improved by optimizing the parameters such as transmission power, sensing bandwidth, and power spectral density using the Fractional GWO-CS optimization algorithm. With the proposed novel FOM, the spectrum holes can be detected with the optimized transmission power, sensing bandwidth, and power spectral density values. The proposed model will be implemented in the working platform of Matlab by optimizing the energy efficiency of spectrum sensing in terms of transmission power, spectrum sensing bandwidth, and power spectral density compared which will be compared with existing optimization methods.

scholarly journals Precoding Design for Energy Efficiency Maximization in MIMO Half-Duplex Wireless Sensor Networks with SWIPT

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4923 ◽  
Author(s):  
Liang Xue ◽  
Jin-Long Wang ◽  
Jie Li ◽  
Yan-Long Wang ◽  
Xin-Ping Guan
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Energy Harvesting ◽  
Sensor Node ◽  
Optimization Problem ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Maximization Problem ◽  
Half Duplex ◽  
Energy Constrained

This paper explores the energy efficiency (EE) maximization problem in single-hop multiple-input multiple-output (MIMO) half-duplex wireless sensor networks (WSNs) with simultaneous wireless information and power transfer (SWIPT). Such an energy efficiency maximization problem is considered in two different scenarios, in which the number of energy-harvesting (EH) sensor nodes are different. In the scenario where the single energy-harvesting sensor node is applied, the modeled network consists of two multiple-antenna transceivers, of which the energy-constrained energy-harvesting sensor node harvests energy from the signals transmitted from the source by a power splitting (PS) scheme. In the scenario of multiple EH sensor nodes, K energy-constrained sensor nodes are applied and the same quantity of antennas are equiped on each of them. The optimization problem is formulated to maximize the energy efficiency by jointly designing the transceivers’ precoding matrices and the PS factor of the energy-harvesting sensor node. The considered constraints are the required harvested energy, the transmission power limit and the requirement on the data rate. The joint design of the precoding matrices and the PS factor can be formulated as an optimization problem, which can be transformed into two sub-problems. An alternating algorithm based on Dinkelbach is proposed to solve the two sub-problems. The convergence of the proposed alternating algorithm, the solution optimality and the computational complexity are analyzed in the paper. Simulation results demonstrate the convergence and effectiveness of our proposed algorithm for realizing the maximum energy efficiency.

scholarly journals Adaptive Energy Efficiency Maximization for Cognitive Underwater Acoustic Network under Spectrum Sensing Errors and CSI Uncertainties

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yaohui Wu ◽  
Youming Li ◽  
Qingpeng Yao
Keyword(s):  
Energy Efficiency ◽  
Spectrum Sensing ◽  
Primary User ◽  
Tolerance Factor ◽  
Maximization Problem ◽  
Dual Decomposition ◽  
Power Constraint ◽  
Worst Case ◽  
Underwater Acoustic ◽  
Underwater Acoustic Network

Energy efficiency (EE) maximization problem for Cognitive Underwater Acoustic Network is investigated in this study. Available works on EE usually assume that spectrum sensing is accurate or that channel state information (CSI) is perfect, which is often impractical. Thus, an adaptive resource allocation scheme is proposed to maximize the EE, subject to the transmission power constraint of secondary user (SU) and the interference power constraint of primary user (PU). By taking the spectrum sensing errors into account, we add power interference from PU to SU in the objective function. Besides, interference tolerance factor is introduced to control the interference from SU to PU. Assuming CSI uncertainties of the involved channels are bounded, they are separately modeled as stochastic-case or worst-case according to their nature. Since the established optimization problem is nonconvex, it is converted into a convex one and then solved by the techniques of fractional programming and dual decomposition. Simulation results validate that the EE can be improved by classifying the CSI uncertainties and solving the expectation of the CSI correlation function. Furthermore, the interference from SU to PU can be controlled well by the adjustment of the interference tolerance factor.

Perpetual Topology Control in Energy Harvesting Sensor Network

2014 ◽  
Vol 556-562 ◽  
pp. 2487-2491
Author(s):  
Chao Yang Lee ◽  
Chu Sing Yang
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Renewable Energy ◽  
Energy Harvesting ◽  
Sensor Network ◽  
Energy Efficient ◽  
Topology Control ◽  
Wireless Sensor ◽  
Sufficient Energy ◽  
Sustained Operation

Harvesting ambient energy to power Wireless Sensor Networks (WSNs) is a promising approach. However, due to low recharging rates and the dynamics of renewable energy, energy harvesting sensors are unable to provide sufficient energy for sustained operation. This work designs a novel perpetual topology control that can enhance the energy efficiency and prolong network lifetime in energy harvesting sensor network. The proposed perpetual topology control (PTC) algorithm aims to ensure WSN sustainability and make the harvesting ambient energy usefully. Experimental results demonstrate the superiority of the PDTC algorithm in energy efficient.

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